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Inferring Disease-Associated MicroRNAs Using Semi-supervised Multi-Label Graph Convolutional Networks.

Xiaoyong Pan1, Hong-Bin Shen2

  • 1Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, 200240 Shanghai, China; Department of Medical informatics, Erasmus Medical Center, 3015 CE Rotterdam, the Netherlands.

Iscience
|October 13, 2019
PubMed
Summary

We developed DimiG, a computational method using Graph Convolutional Networks, to identify disease-associated microRNAs (miRNAs). DimiG effectively predicts miRNA-disease links by integrating various biological data, aiding disease research.

Keywords:
Biocomputational MethodComputer ModelingDiseaseGene Network

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • MicroRNAs (miRNAs) are key regulators in biological processes and disease development.
  • While protein-coding gene (PCG)-disease associations are known, miRNA-disease links require further investigation.
  • miRNAs interact with PCGs, influencing their function and disease pathways.

Purpose of the Study:

  • To develop a computational method, DimiG, for inferring miRNA-associated diseases.
  • To leverage a semi-supervised Graph Convolutional Network (GCN) model for predicting miRNA-disease associations.
  • To integrate diverse biological data for enhanced prediction accuracy.

Main Methods:

  • DimiG employs a multi-label GCN framework.
  • It integrates PCG-PCG interactions, PCG-miRNA interactions, PCG-disease associations, and tissue expression profiles.
  • The model is trained on known disease-PCG associations and interaction networks.

Main Results:

  • DimiG effectively scores associations between diseases and miRNAs.
  • Performance evaluation on a benchmark dataset shows DimiG outperforms unsupervised methods and rivals supervised methods.
  • Case studies on prostate cancer, lung cancer, and inflammatory bowel disease validate DimiG's predictions with existing literature.

Conclusions:

  • DimiG is an effective computational tool for predicting miRNA-disease associations.
  • The method provides valuable insights into the roles of miRNAs in various diseases.
  • DimiG aids in discovering novel miRNA biomarkers and therapeutic targets for diseases.